VS298: Natural Scene Statistics

This seminar will examine what is known about the statistical structure of natural visual and auditory scenes, and theories of how sensory coding strategies have been adapted to this structure.

Instructor: Bruno Olshausen

Enrollment information:

VS 298 (section 4), 2 units
CCN: 66489

Meeting time and place:

Monday 6-8, Evans 560

Email list:

nss2014@lists.berkeley.edu subscribe

Readings:

Books and review articles:

• Natural Image Statistics by Hyvarinen, Hurri & Hoyer
• Olshausen BA & Lewicki MS (2013) What natural scene statistics can tell us about cortical representation. In: The Cognitive Neurosciences V. paper
• Geisler WS (2008) Visual perception and the statistical properties of natural scenes. Annual Review of Psychology paper

Weekly schedule:

Date	Topic/Reading	Presenter
Feb. 3	Redundancy reduction, whitening, and power spectrum of natural images Barlow (1961): Theory of redundancy reduction paper Atick (1992): Theory of whitening paper Field (1987): 1/f2 power spectrum and sparse coding paper Additional reading: Attneave (1954) - 'Some informational aspects of visual perception' paper Laughlin (1981) - Histogram equalization of contrast response paper Srinivasan (1982) - 'Predictive coding: a fresh view of inhibition in the retina' paper Switkes (1978) - Power spectrum of carpentered environments paper Ruderman (1997) - Why are images 1/f2? paper Torralba & Oliva (2003) - Power spectrum of natural image categories paper	Anthony DiFranco Dylan Paiton Michael Levy
Feb. 10	Whitening in time and color; Robust coding Dong & Atick (1995): spatiotemporal power spectrum of natural movies paper Ruderman (1998): statistics of cone responses paper Karklin & Simoncelli (2012): noisy population coding of natural images paper Additional reading: Dong & Atick (1995) - spatiotemporal decorrelation using lagged and non-lagged cells paper Doi & Lewicki (2007) - A theory of retinal population coding paper	Chayut Thanapirom Michael Levy Yubei Chen
Feb. 17	** Holiday **
Feb. 24	Higher-order statistics and sensory coding Barlow (1972): Sparse coding paper Field (1994): What is the goal of sensory coding? paper Bell & Sejnowski (1995): Independent component analysis. paper Additional reading: Redlich (1993): Redundancy Reduction as a Strategy for Unsupervised Learning. paper Baddeley (1996): Searching for filter with 'interesting' output distributions: An uninteresting direction to explore? paper O'regan & Noe (2001): A sensorimotor account of vision and visual consciousness paper	Karl Zipser Michael Levy Mayur Mudigonda
March 3	ICA and sparse coding of natural images Bell & Sejnowski (1997): ICA of natural images paper Olshausen & Field (1997): Sparse coding of natural images paper van Hateren & Ruderman (1998), Olshausen (2003): ICA/sparse coding of natural video paper1, paper2	Mayur Mudigonda Zayd Enam Georgios Exarchakis
March 10	Statistics of natural sound and auditory coding Clark & Voss: '1/f noise and music' Smith & Lewicki: sparse coding of natural sound Klein/Deweese: ICA/sparse coding of spectrograms	Tyler Lee Yubei Chen TBD
March 17	Higher-order group structure Geisler: contour statistics Hyvarinen: subspace ICA/topgraphic ICA Lyu & Simoncelli: radial Gaussianization	Chayut Thanapirom Guy Isely TBD
March 24	** Spring recess **
March 31	Energy-based models Hinton: Restricted Boltzmann machine Osindero & Hinton: Product of Experts Roth & Black: Fields of experts	Evan Shelhamer Brian Cheung Chris Warner
April 7	Learning invariances through 'slow feature analysis' Foldiak/Wiskott: slow feature analysis Hyvarinen: 'Bubbles' Berkes et al.: factorizing 'what' and 'where' from video	Guy Isely Chayut Thanapirom TBD
April 14	Manifold and Lie group models Carlsson: Klein bottle model of natural images Culpepper & Olshausen: Learning manifold transport operators Roweis & Saul: Local Linear Embedding	TBD TBD TBD
April 21	Hierarchical models Karklin & Lewicki (2003): density components Shan & Cottrell: stacked ICA Cadieu & Olshausen (2012): learning intermediate representations of form and motion
April 28	Deep network models Hinton & Salakhudinov (2006): stacked RBMs Le et al. (2011): Unsupervised learning (Google brain, 'cat' neurons) Krishevsky et al. (2012): Supervised learning, ImageNet 1000 paper Fergus (2013): visualizing what deep nets learn paper	TBD TBD Reza Abbasi-Asl Shiry Ginosar
May 5	Special topics Image compression with Hopfield networks	Chris Hillar
May 12	Special topics